US6636643B1 - System and method for improving compressed image appearance using stochastic resonance and energy replacement - Google Patents

System and method for improving compressed image appearance using stochastic resonance and energy replacement Download PDF

Info

Publication number
US6636643B1
US6636643B1 US09/498,925 US49892500A US6636643B1 US 6636643 B1 US6636643 B1 US 6636643B1 US 49892500 A US49892500 A US 49892500A US 6636643 B1 US6636643 B1 US 6636643B1
Authority
US
United States
Prior art keywords
data
function
image
linearizing
dequantifying
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
US09/498,925
Inventor
Kenbe D. Goertzen
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
QU VIS Inc
QUVIS TECHNOLOGIES Inc
QuVis Inc
Original Assignee
QuVis Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by QuVis Inc filed Critical QuVis Inc
Priority to US09/498,925 priority Critical patent/US6636643B1/en
Assigned to QU VIS, INC. reassignment QU VIS, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: GOERTZEN, KENBE D.
Application granted granted Critical
Publication of US6636643B1 publication Critical patent/US6636643B1/en
Assigned to MTV CAPITAL LIMITED PARTNERSHIP reassignment MTV CAPITAL LIMITED PARTNERSHIP SECURITY AGREEMENT Assignors: QUVIS, INC.
Assigned to SEACOAST CAPITAL PARTNERS II, L.P., A DELAWARE LIMITED PARTNERSHIP reassignment SEACOAST CAPITAL PARTNERS II, L.P., A DELAWARE LIMITED PARTNERSHIP INTELLECTUAL PROPERTY SECURITY AGREEMENT TO THAT CERTAIN LOAN AGREEMENT Assignors: QUVIS, INC., A KANSAS CORPORATION
Assigned to QUVIS TECHNOLOGIES, INCORPORATED reassignment QUVIS TECHNOLOGIES, INCORPORATED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SEACOAST CAPITAL PARTNERS II LP
Assigned to SEACOAST CAPITAL PARTNERS II LP reassignment SEACOAST CAPITAL PARTNERS II LP RELEASE OF MTV CAPITAL AND SEACOAST SECURITY LIEN AND ASSIGNMENT THROUGH BANKRUPTCY Assignors: QUVIS, INCORPORATED
Anticipated expiration legal-status Critical
Expired - Fee Related legal-status Critical Current

Links

Classifications

    • G06T5/70—
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/48—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using compressed domain processing techniques other than decoding, e.g. modification of transform coefficients, variable length coding [VLC] data or run-length data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H04N19/527—Global motion vector estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
    • H04N19/86—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving reduction of coding artifacts, e.g. of blockiness
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals

Definitions

  • This invention related to image processing methods and more particular to a system and method for using stochastic resonance and image replacement to improve the appearance of a compressed image.
  • Discrete Cosine Transforms of images is a technique widely used to reduce the amount of data in an image file. This consists of transforming parts of the image, typically blocks of 8 ⁇ 8 or 16 ⁇ 16 pixels. This method is limited in efficiency because it does not take full advantage of features which cross block boundaries, and it tends to generate tiled artifacts at block boundaries which are very obvious to viewers.
  • Compression methods such as full image wavelet transforms, can be used to avoid some of the traditional classes of artifacts.
  • the resulting images can still have an unnatural appearance due to the lack of noise and due to image elements blurring together unnaturally as a result of the subband or wavelet processing.
  • the quantified data sets do not adequately represent the original continuous image.
  • the system and method of the present invention take advantage of a stochastic resonance-type process to improve the information transfer, compensate for the quantification error, and replace any energy removed by the quantification process with the appropriate uncertainty in the output. In this way, complete compensation for any unnatural appearance in the resulting image due to quantification error is achieved.
  • the resulting image has the appearance of being seen or photographed under progressively worse lighting conditions. It retains its natural appearance, while the information content and perceived quality degrades in a very natural and acceptable manner, with none of the traditional compression artifacts.
  • the present invention manipulates the component data classes resulting from subband partitioning of an image or signal to improve the efficiency and characteristics of the resulting signal.
  • the result can most easily be described as producing a “film look” as a result of how quantification uncertainty is manipulated.
  • a subband transform is performed on an image generating components that represent either high or low and DC frequency components.
  • the data components which contain DC information are linearized by the addition of a function that has a probability and magnitude distribution similar to the quantification error.
  • the linearizing function is a generator of random values chosen so that it is exactly reproducible and provides a reasonably close model of the error distribution.
  • the data is then dequantified by the reverse process, which can range from a simple linear multiplication, to complex nonlinear, adaptive, and table lookup processes.
  • a method which linearizes large magnitude signals only and can avoid the increased size problem by operating on values which result in quantified magnitudes other than 0.
  • the linearizing function distribution is chosen as in the preferred embodiment but is now used to modulate the quantification function around zero.
  • the selected linearizing function distribution will have a minimum range matching the original zero range and a maximum range equal to the original range plus the linearizing function range. This results in an increase in the number of zero values while at the same time linearizing the quantifier for large magnitude values.
  • the result is then dequantified by reproducing the linearizing function and reversing the quantification process.
  • a dither function with a distribution similar to the quantification error may be used as the range resulting from dequantification of zero values is not linearized.
  • the subbands are then recombined to create a new image with the enhanced “film look”.
  • the present invention manipulates the component data classes resulting from subband partitioning of an image or signal to improve the efficiency and characteristics of the resulting signal.
  • the result can most easily be described as producing a “film look” as a result of how quantification uncertainty is manipulated.
  • the following steps will be described as being performed on a computer for illustrations purposes only. As is well known in the art, any apparatus capable of performing the necessary steps and calculations may be used.
  • the method begins by applying a subband transform to the image. After a subband transform there are two or more sets of components representing various frequency components of the original image. In the discussion below a distinction is made between the bands of component data that contain DC and low frequency information and the bands that contain only high frequency information.
  • the data which contains DC information is linearized by the addition of a function with a probability and magnitude distribution similar to the quantification error.
  • the quantification error will depend on implementation details but will typically be a distribution of error values centered on an error value of 0 and the values will predominantly fall in the range of plus or minus the quantification range over two.
  • the quantification range may be a function of the original value's magnitude or may be a function based on other features.
  • the linearizing function is a generator of random values chosen so that it is exactly reproducible and provides a reasonably close model of the error distribution. If the magnitude of the linearizing function is chosen so that it is the largest magnitude distribution which has little if any impact on the encoded size, the point of maximum information transfer is realized.
  • the data is then dequantified by the reverse process, which can range from a simple linear multiplication, to complex nonlinear, adaptive, and table lookup processes. After dequantification, the linearizing function is exactly reproduced and subtracted from the result by repeating the generating process. This produces a result with more resolution on a statistical basis than would have been present otherwise.
  • a method which linearizes large magnitude signals only and can avoid the increased size problem by operating on values which result in quantified magnitudes other than 0.
  • a model of the quantification error should be added to zero values.
  • linearizing function is used to control the amount of coring which occurs around zero.
  • the linearizing function distribution is chosen as before, but is now used to modulate the quantification function around zero, with a minimum range matching the original zero range, and a maximum range equal to the original plus the linearizing function range. This results in an increase in the number of zero values while at the same time linearizing the quantifier for large magnitude values.
  • linearizing function magnitude can be chosen as that magnitude which does not significantly increase the resulting size.
  • the result may then be dequantified by reproducing the linearizing function and reversing the quantification process.
  • a dither function with a distribution similar to the quantification error can be added, because the range resulting from dequantification of zero values has not been linearized.
  • This process linearizes the resolution of the signal, optimize the information transfer through the quantifier and entropy coder, in the resulting image, accurately represents the uncertainty introduced during image capture or the “film look”.

Abstract

A method for improving the image quality of a compressed image is provided. The improvement relates to the addition of certain error components resulting a less “clean” image, also referred to as the “film look”. The system and method perform a subband transforms on one or more images, wherein the image is split into one or more high frequency or low and DC frequency components. Responsive to a component containing DC frequencies, the data in that component is linearized. The linearized data in that component is then dequantified. Preferably, linearizing the data includes adding a function that has a probability and magnitude distribution similar to quantification error of the data. Furthermore, linearizing the data may include using a function that is exactly reproducible and generates random values that closely model the quantification error distribution.

Description

RELATED APPLICATION
This Application claims benefit of provisional No. 60/118,571 filed Feb. 4, 1998.
The subject matter of this application is related to the subject matter of the following commonly owned applications: Ser. No. 09/112,668, now U.S. Pat. No. 6,298,160 titled “Apparatus And Method For Entropy Coding”, filed on Jul. 9, 1998, also by Kenbe Goertzen; Ser. No. 09/498,323, titled “Scalable Resolution Motion Image Recording And Storage System”, filed concurrently”, also by Kenbe Goertzen; Ser. No. 09/498,925, titled “A System And Method For Improving Compressed Image Appearance Using Stochastic Resonance And Energy Replacement”, filed concurrently”, also by Kenbe Goertzen; Ser. No. 09/498,924, now U.S. Pat. No. 6,532,308 titled “Quality Priority Image Storage and Communication”, filed concurrently”, also by Kenbe Goertzen: the contents of which are incorporated by reference as if fully disclosed herein.
FIELD OF INVENTION
This invention related to image processing methods and more particular to a system and method for using stochastic resonance and image replacement to improve the appearance of a compressed image.
SUMMARY OF THE RELATED ART
Various compression approaches are in use today which generate undesirable artifacts. These artifacts are often on fixed boundaries, or consist of patterns uncommon in natural images. For example, Discrete Cosine Transforms of images is a technique widely used to reduce the amount of data in an image file. This consists of transforming parts of the image, typically blocks of 8×8 or 16×16 pixels. This method is limited in efficiency because it does not take full advantage of features which cross block boundaries, and it tends to generate tiled artifacts at block boundaries which are very obvious to viewers.
Compression methods, such as full image wavelet transforms, can be used to avoid some of the traditional classes of artifacts. The resulting images can still have an unnatural appearance due to the lack of noise and due to image elements blurring together unnaturally as a result of the subband or wavelet processing. At extreme compression levels, the quantified data sets do not adequately represent the original continuous image. These result in artifacts described as “flat”, “blurry”, “lumpy”, “speckled”, “ringing”, or “evaporating”.
SUMMARY OF INVENTION
The system and method of the present invention take advantage of a stochastic resonance-type process to improve the information transfer, compensate for the quantification error, and replace any energy removed by the quantification process with the appropriate uncertainty in the output. In this way, complete compensation for any unnatural appearance in the resulting image due to quantification error is achieved. As the compression is increased, the resulting image has the appearance of being seen or photographed under progressively worse lighting conditions. It retains its natural appearance, while the information content and perceived quality degrades in a very natural and acceptable manner, with none of the traditional compression artifacts.
The present invention manipulates the component data classes resulting from subband partitioning of an image or signal to improve the efficiency and characteristics of the resulting signal. The result can most easily be described as producing a “film look” as a result of how quantification uncertainty is manipulated.
In the preferred embodiment of the present invention, a subband transform is performed on an image generating components that represent either high or low and DC frequency components. The data components which contain DC information are linearized by the addition of a function that has a probability and magnitude distribution similar to the quantification error. The linearizing function is a generator of random values chosen so that it is exactly reproducible and provides a reasonably close model of the error distribution. In the final step, the data is then dequantified by the reverse process, which can range from a simple linear multiplication, to complex nonlinear, adaptive, and table lookup processes.
In an alternative embodiment, a method is provided which linearizes large magnitude signals only and can avoid the increased size problem by operating on values which result in quantified magnitudes other than 0. In this embodiment, the linearizing function distribution is chosen as in the preferred embodiment but is now used to modulate the quantification function around zero. Ideally the selected linearizing function distribution will have a minimum range matching the original zero range and a maximum range equal to the original range plus the linearizing function range. This results in an increase in the number of zero values while at the same time linearizing the quantifier for large magnitude values. The result is then dequantified by reproducing the linearizing function and reversing the quantification process. To dequantify zero values, a dither function with a distribution similar to the quantification error may be used as the range resulting from dequantification of zero values is not linearized. In both cases, the subbands are then recombined to create a new image with the enhanced “film look”.
BRIEF DESCRIPTION OF THE DRAWINGS (None) DETAILED DESCRIPTION
The present invention manipulates the component data classes resulting from subband partitioning of an image or signal to improve the efficiency and characteristics of the resulting signal. The result can most easily be described as producing a “film look” as a result of how quantification uncertainty is manipulated. The following steps will be described as being performed on a computer for illustrations purposes only. As is well known in the art, any apparatus capable of performing the necessary steps and calculations may be used.
The method begins by applying a subband transform to the image. After a subband transform there are two or more sets of components representing various frequency components of the original image. In the discussion below a distinction is made between the bands of component data that contain DC and low frequency information and the bands that contain only high frequency information.
In the quantification step, which may utilize a number of processes including linear division, complex nonlinear functions, adaptive, or table lookup processes, the data which contains DC information is linearized by the addition of a function with a probability and magnitude distribution similar to the quantification error. The quantification error will depend on implementation details but will typically be a distribution of error values centered on an error value of 0 and the values will predominantly fall in the range of plus or minus the quantification range over two. The quantification range may be a function of the original value's magnitude or may be a function based on other features.
The linearizing function is a generator of random values chosen so that it is exactly reproducible and provides a reasonably close model of the error distribution. If the magnitude of the linearizing function is chosen so that it is the largest magnitude distribution which has little if any impact on the encoded size, the point of maximum information transfer is realized. The data is then dequantified by the reverse process, which can range from a simple linear multiplication, to complex nonlinear, adaptive, and table lookup processes. After dequantification, the linearizing function is exactly reproduced and subtracted from the result by repeating the generating process. This produces a result with more resolution on a statistical basis than would have been present otherwise.
The same method can be used for the high frequency components, but the magnitude of a linearizing function which does not significantly increase the encoded size will typically be very low, resulting in little advantage. This is because of the large population of exactly zero values in these components after quantification.
In an alternative embodiment, a method is provided which linearizes large magnitude signals only and can avoid the increased size problem by operating on values which result in quantified magnitudes other than 0. In order for zero values to appear to result in similar distributions appropriate to the quantification error after dequantification, a model of the quantification error should be added to zero values.
One way to implement this is to use the linearizing function to control the amount of coring which occurs around zero. In this case, the linearizing function distribution is chosen as before, but is now used to modulate the quantification function around zero, with a minimum range matching the original zero range, and a maximum range equal to the original plus the linearizing function range. This results in an increase in the number of zero values while at the same time linearizing the quantifier for large magnitude values. Once again, linearizing function magnitude can be chosen as that magnitude which does not significantly increase the resulting size. The result may then be dequantified by reproducing the linearizing function and reversing the quantification process. To dequantify zero values, a dither function with a distribution similar to the quantification error can be added, because the range resulting from dequantification of zero values has not been linearized.
This process linearizes the resolution of the signal, optimize the information transfer through the quantifier and entropy coder, in the resulting image, accurately represents the uncertainty introduced during image capture or the “film look”.

Claims (15)

I claim:
1. A method for improving the image quality of a compressed image, the method including the steps of:
a. performing a subband transform on the image, wherein the image is split into one or more high frequency or low and DC frequency components;
b. quantizing the components; and
c. applying a linearizing function to at least one component, the linearizing function having a probability distribution that is substantially that of quantification error to add noise into the signal that was removed during quantization.
2. The method of claim 1, wherein the step of linearizing includes using a function that it is exactly reproducible and generates random values that closely model the quantification error distribution.
3. The method of claim 1, further comprising dequantifying the data, wherein the step of dequantifying the data includes a simple linear multiplication algorithm.
4. The method of claim 1, further comprising dequantifying the data wherein the step of dequantifying the data includes applying a complex nonlinear dequanalgorithm.
5. The method of claim 1, further comprising dequantifying the data wherein the step of dequantifying the data includes applying an adaptive dequantification algorithm.
6. The method of claim 1, further comprising dequantifying the data wherein the step of dequantifying the data includes a table lookup process.
7. A system for improving the image quality of a compressed image, the system including:
a. a processor, wherein the processor is capable of performing the steps of:
i. performing a subband transform on the image such that the image is divided into frequency components;
ii. linearizing the data in at least one component after the data has been quantized to add noise into the image that was removed during quantization;
iii.
b. coupled to the processor, one or more memory modules for dynamically storing the image;
c. coupled to the memory, function memory for storing one or more equations used to linearize data of the image; and
d. coupled to the processor, memory modules and function memory, a communication bus that provides a digital communications link between the processor, the memory modules, and the function memory of the system.
8. The system of claim 7, wherein the function memory includes one or more function that it are exactly reproducible and generate random values that closely model the quantification error distribution for a given image data sample.
9. The system of claim 7, wherein the function memory includes a simple linear multiplication algorithm.
10. The system of claim 7, wherein the function memory includes a complex nonlinear dequantification algorithm.
11. The system of claim 7, wherein the function memory includes an adaptive dequantification algorithm.
12. The method according to claim 1, wherein the linearizing function has a first part and a second part wherein in the step of applying the linearizing function, the first part is applied to data having a magnitude above a threshold wherein the first part has a function having a probability distribution that is substantially that of quantification error and the second part is applied to data having a magnitude below a threshold and the second part increases the number of zero values in the data.
13. The method according to claim 12, further comprising:
applying a dithering function having a probability distribution that is substantially that of quantification error to data that was originally below the threshold; and
dequantifying the data.
14. The system according to claim 7, wherein in the processor the linearizing function has a first part and a second part wherein in the step of applying the linearizing function, the first part is applied to data having a magnitude above a threshold wherein the first part has a function having a probability distribution that is substantially that of quantification error and the second part is applied to data having a magnitude below a threshold and the second part increases the number of zero values in the data.
15. The system according to claim 14, wherein the processor upon decoding applies a dithering function having a probability distribution that is substantially that of quantification error to data that was originally below the threshold; and dequantifies the data.
US09/498,925 1999-02-04 2000-02-04 System and method for improving compressed image appearance using stochastic resonance and energy replacement Expired - Fee Related US6636643B1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US09/498,925 US6636643B1 (en) 1999-02-04 2000-02-04 System and method for improving compressed image appearance using stochastic resonance and energy replacement

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US11857199P 1999-02-04 1999-02-04
US09/498,925 US6636643B1 (en) 1999-02-04 2000-02-04 System and method for improving compressed image appearance using stochastic resonance and energy replacement

Publications (1)

Publication Number Publication Date
US6636643B1 true US6636643B1 (en) 2003-10-21

Family

ID=22379425

Family Applications (1)

Application Number Title Priority Date Filing Date
US09/498,925 Expired - Fee Related US6636643B1 (en) 1999-02-04 2000-02-04 System and method for improving compressed image appearance using stochastic resonance and energy replacement

Country Status (7)

Country Link
US (1) US6636643B1 (en)
EP (1) EP1198780A4 (en)
JP (1) JP2002536897A (en)
KR (1) KR20010101951A (en)
AU (1) AU772599B2 (en)
CA (1) CA2361413A1 (en)
WO (1) WO2000046741A1 (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030142875A1 (en) * 1999-02-04 2003-07-31 Goertzen Kenbe D. Quality priority
US20030185455A1 (en) * 1999-02-04 2003-10-02 Goertzen Kenbe D. Digital image processor
US6823129B1 (en) * 2000-02-04 2004-11-23 Quvis, Inc. Scaleable resolution motion image recording and storage system
CN109118472A (en) * 2018-07-03 2019-01-01 杭州电子科技大学 A kind of eye fundus image blood vessel random resonance detection method that adaptive scale decomposes
CN110275157A (en) * 2019-06-20 2019-09-24 西北工业大学 Vector acoustic bearing method based on genetic algorithm self-adapting random resonant
CN112904434A (en) * 2020-12-22 2021-06-04 电子科技大学 Magnetic anomaly signal detection method based on parameter optimization stochastic resonance

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6669397B2 (en) 2016-03-31 2020-03-18 キヤノン株式会社 Signal extraction processing device and signal extraction processing method
JP6732525B2 (en) * 2016-05-02 2020-07-29 キヤノン株式会社 Image processing apparatus and image processing method

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5454051A (en) 1991-08-05 1995-09-26 Eastman Kodak Company Method of reducing block artifacts created by block transform compression algorithms
US5852682A (en) 1995-02-28 1998-12-22 Daewoo Electronics, Co., Ltd. Post-processing method and apparatus for use in a video signal decoding apparatus
US5907636A (en) 1995-11-24 1999-05-25 Nec Corporation Image signal decoder
US5974186A (en) * 1995-10-24 1999-10-26 Georgia Tech Research Corporation Video coding system and method for noisy signals
US6134350A (en) * 1998-02-18 2000-10-17 Dome Imaging Systems, Inc. Method of producing wavelets and compressing digital images and of restoring the digital images
US6141446A (en) * 1994-09-21 2000-10-31 Ricoh Company, Ltd. Compression and decompression system with reversible wavelets and lossy reconstruction
US6215422B1 (en) * 1997-08-29 2001-04-10 Canon Kabushiki Kaisha Digital signal huffman coding with division of frequency sub-bands
US6298160B1 (en) * 1997-07-09 2001-10-02 Quvis, Inc. Apparatus and method for entropy coding

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5748786A (en) * 1994-09-21 1998-05-05 Ricoh Company, Ltd. Apparatus for compression using reversible embedded wavelets

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5454051A (en) 1991-08-05 1995-09-26 Eastman Kodak Company Method of reducing block artifacts created by block transform compression algorithms
US6141446A (en) * 1994-09-21 2000-10-31 Ricoh Company, Ltd. Compression and decompression system with reversible wavelets and lossy reconstruction
US5852682A (en) 1995-02-28 1998-12-22 Daewoo Electronics, Co., Ltd. Post-processing method and apparatus for use in a video signal decoding apparatus
US5974186A (en) * 1995-10-24 1999-10-26 Georgia Tech Research Corporation Video coding system and method for noisy signals
US5907636A (en) 1995-11-24 1999-05-25 Nec Corporation Image signal decoder
US6298160B1 (en) * 1997-07-09 2001-10-02 Quvis, Inc. Apparatus and method for entropy coding
US6215422B1 (en) * 1997-08-29 2001-04-10 Canon Kabushiki Kaisha Digital signal huffman coding with division of frequency sub-bands
US6134350A (en) * 1998-02-18 2000-10-17 Dome Imaging Systems, Inc. Method of producing wavelets and compressing digital images and of restoring the digital images

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030142875A1 (en) * 1999-02-04 2003-07-31 Goertzen Kenbe D. Quality priority
US20030185455A1 (en) * 1999-02-04 2003-10-02 Goertzen Kenbe D. Digital image processor
US6823129B1 (en) * 2000-02-04 2004-11-23 Quvis, Inc. Scaleable resolution motion image recording and storage system
CN109118472A (en) * 2018-07-03 2019-01-01 杭州电子科技大学 A kind of eye fundus image blood vessel random resonance detection method that adaptive scale decomposes
CN109118472B (en) * 2018-07-03 2021-06-08 杭州电子科技大学 Self-adaptive scale-decomposed fundus image blood vessel stochastic resonance detection method
CN110275157A (en) * 2019-06-20 2019-09-24 西北工业大学 Vector acoustic bearing method based on genetic algorithm self-adapting random resonant
CN110275157B (en) * 2019-06-20 2023-02-14 西北工业大学 Vector sound orientation method based on genetic algorithm self-adaptive random resonance
CN112904434A (en) * 2020-12-22 2021-06-04 电子科技大学 Magnetic anomaly signal detection method based on parameter optimization stochastic resonance

Also Published As

Publication number Publication date
AU2872000A (en) 2000-08-25
EP1198780A4 (en) 2005-08-17
WO2000046741A1 (en) 2000-08-10
AU772599B2 (en) 2004-05-06
CA2361413A1 (en) 2000-08-10
EP1198780A1 (en) 2002-04-24
JP2002536897A (en) 2002-10-29
KR20010101951A (en) 2001-11-15

Similar Documents

Publication Publication Date Title
EP0590922B1 (en) Improved decompression of standard ADCT-compressed images
US5495538A (en) Segmentation-based JPEG image artifacts reduction
EP0635985B1 (en) Improved decompression standard for ADCT-compressed document images
JP3933691B2 (en) Hybrid waveform and model-based encoding and decoding of image signals
EP0422404A2 (en) Transform coding using coefficient prediction techniques
JPH11168633A (en) Reconstruction execution method, reconstruction execution device, record medium, inverse conversion execution method, inverse conversion execution device, suitable reconstruction generating method, suitable reconstruction generator, coding data processing method, coding data processing unit and data processing system
EP0491779A4 (en) Improved image compression method and apparatus
JPH01109979A (en) Image data sorting for digital tv signal
US5719961A (en) Adaptive technique for encoder and decoder signal transformation
JPH05219385A (en) Picture compression expansion method and device
KR20010102155A (en) Reducing 'Blocky picture' effects
US5521718A (en) Efficient iterative decompression of standard ADCT-compressed images
US6636643B1 (en) System and method for improving compressed image appearance using stochastic resonance and energy replacement
JP2901656B2 (en) Image coding device
Vander Kam et al. JPEG-compliant perceptual coding for a grayscale image printing pipeline
US20040136600A1 (en) Visually lossless still image compression for RGB, YUV, YIQ, YCrCb, K1K2K3 formats
KR102471796B1 (en) Method and system for preprocessing cognitive video using saliency map
KR100195187B1 (en) Apparatus and method of compressing still image at steady compression rate
JP2891251B2 (en) Image encoding device and image decoding device
Ngan et al. A HVS-weighted cosine transform coding scheme with adaptive quantization
KR100744442B1 (en) Improved cascaded compression method and system for digital video and images
JP3199786B2 (en) Image processing device
Carey et al. Smoothness-constrained quantization for wavelet image compression
D'Alessandro et al. Pyramid vector quantization applied to a video coding scheme for contribution quality
JPH08205152A (en) Image compressing and encoding device

Legal Events

Date Code Title Description
AS Assignment

Owner name: QU VIS, INC., KANSAS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:GOERTZEN, KENBE D.;REEL/FRAME:010806/0673

Effective date: 20000510

AS Assignment

Owner name: MTV CAPITAL LIMITED PARTNERSHIP, OKLAHOMA

Free format text: SECURITY AGREEMENT;ASSIGNOR:QUVIS, INC.;REEL/FRAME:018847/0219

Effective date: 20070202

FPAY Fee payment

Year of fee payment: 4

AS Assignment

Owner name: SEACOAST CAPITAL PARTNERS II, L.P., A DELAWARE LIM

Free format text: INTELLECTUAL PROPERTY SECURITY AGREEMENT TO THAT CERTAIN LOAN AGREEMENT;ASSIGNOR:QUVIS, INC., A KANSAS CORPORATION;REEL/FRAME:021824/0260

Effective date: 20081111

FEPP Fee payment procedure

Free format text: PAT HOLDER NO LONGER CLAIMS SMALL ENTITY STATUS, ENTITY STATUS SET TO UNDISCOUNTED (ORIGINAL EVENT CODE: STOL); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

REMI Maintenance fee reminder mailed
AS Assignment

Owner name: SEACOAST CAPITAL PARTNERS II LP, MASSACHUSETTS

Free format text: RELEASE OF MTV CAPITAL AND SEACOAST SECURITY LIEN AND ASSIGNMENT THROUGH BANKRUPTCY;ASSIGNOR:QUVIS, INCORPORATED;REEL/FRAME:026551/0845

Effective date: 20101228

Owner name: QUVIS TECHNOLOGIES, INCORPORATED, KANSAS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SEACOAST CAPITAL PARTNERS II LP;REEL/FRAME:026549/0807

Effective date: 20110307

FPAY Fee payment

Year of fee payment: 8

SULP Surcharge for late payment

Year of fee payment: 7

REMI Maintenance fee reminder mailed
LAPS Lapse for failure to pay maintenance fees
STCH Information on status: patent discontinuation

Free format text: PATENT EXPIRED DUE TO NONPAYMENT OF MAINTENANCE FEES UNDER 37 CFR 1.362

FP Lapsed due to failure to pay maintenance fee

Effective date: 20151021